SMART WAY TO FORECAST CROP BY COMBINING AI AND GENETIC METHOD

Agriculture remains the backbone of many economies, yet farmers often face uncertainty in predicting crop yields due to changing environmental conditions and limited access to advanced forecasting tools. Traditional methods rely heavily on manual observation and statistical models, which can be time-consuming, error-prone, and insufficient in addressing complex biological interactions. The increasing demand for sustainable food production highlights the need for innovative solutions that combine precision, efficiency, and adaptability. This project introduces a Smart Way to Forecast Crop by integrating Artificial Intelligence with Genetic Algorithms, enabling accurate prediction of crop performance under diverse conditions. The system leverages AI-driven data analysis to process soil, climate, and crop parameters, while genetic methods optimize prediction models for higher accuracy and adaptability. Designed to assist farmers and agricultural planners, the framework reduces uncertainty, enhances resource management, and supports sustainable farming practices. Implemented using Python-based machine learning libraries and genetic optimization techniques, the model ensures scalability and robustness. Beyond forecasting, the system provides actionable insights for crop selection and resource allocation, thereby improving productivity and resilience in agriculture. This approach represents a step toward smarter, data-driven, and sustainable farming ecosystems.

Smart Crop Forecasting, Artificial Intelligence in Agriculture, Genetic Algorithm Optimization, Sustainable Farming, AI-based Prediction Model, Precision Agriculture, Data-driven Crop Management.

1. Deepa, R., Karthick, R., Velusamy, J., & Senthilkumar, R. (2025). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, 92, 103934.
2. Senthilkumar,Dr.P.Venkatakrishnan,Dr.N.Balaji, Intelligent based novel embedded system based IoT Enabled air pollution monitoring system, ELSEVIER Microprocessors and Microsystems Vol.77, June 2020
3. M. Muthalakshmi, N.Mythili, Gurkirpal Singh, R.Senthilkumar (2025). Innovative Approaches for Evaluating Sugarcane Quality: Utilizing Near-Infrared Spectroscopy to Forecast Brix, Pol, and Fiber Content in Commercial Agricultural Domains. Journal of Food Processing, Wiley, https://doi.org/10.1111/jfpe.70233
4. Senthilkumar Ramachandraarjunan, Venkatakrishnan Perumalsamy & Balaji Narayanan 2022, ‘IoT based artificial intelligence indoor air quality monitoring system using enabled RNN algorithm techniques’, in Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2853-2868
5. N. Nagarani, M. Muthalakshmi , E. S. Vinothkumar and R. Senthilkumar (2026) ‘Optimized Contrastive Multi-Level Graph Neural Networks-Based Pigment Epithelial Detachment Detection in OCT images’ International Journal of Information Technology & Decision Making 2026 World Scientific DOI: 10.1142/S0219622026500343
6. Sanitha P C; Syed Nageena Parveen; Shaik Thaherbasha; M. Shanmugapriya; T. Kalaivani; R. Senthilkumar, Transparent Nutrition: An Explainable AI-based Diet Tracking System for Preventing Nutrition-Related Disorders. 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) DOI:10.1109/ICoICI65217.2025.11252549
7. T. Jayasri; M.R. Archana Jenis; P.B. Aswathy; S. Manoranjitham; Christo George; R. Senthilkumar Identity-First Defense in Zero Trust Security Architecture to Protect Cyberspace 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) DOI:10.1109/ICoICI65217.2025.11254505
8. J. Uthayakumar; Swapna; A. Ravikumar; S. Sreeraj; R. Senthilkumar; Babu Pandipati AI-Driven Water Resource Management Systems 2025 2nd International Conference on Computing and Data Science (ICCDS) DOI: 10.1109/ICCDS64403 .2025.11209318
9. R.Swathiramya; V.V.Karthikeyan; P.Sumathi; Sruthy K V; Afreen Hussain; R.Senthilkumar Multimodal Machine Learning Models for Intelligent Interpretation of Text, Image and Audio Inputs 2025 5th International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) DOI:10.1109/ICERECT65215.2025.11377322
10. Srinju.M; Dr.V.Dhanasekaran; S. Guruprasath; Dr.K.Edison Prabhu; K.J Godlin Debby; Dr.R.Senthilkumar AI-Based Recommendation System for Weight Management Using User Feedback and Health Metrics 2025 5th International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) DOI: 10.1109/ICERECT65215.2025.11379842
11. Sun, G., et al. (2020). Remote Sensing Systems for Crop Monitoring. International Journal of Remote Sensing, 41(12), 4567–4578.
12. Huang, H., et al. (2020). Mobile Robotic Platforms for Agricultural Monitoring. Cyborg and Bionic Systems, 3(2), 77–85.
13. Rohmetra, H., et al. (2019). AI-Powered Agricultural Monitoring Frameworks. Computers and Electronics in Agriculture, 162, 123–131.
14. Casalino, G., et al. (2018). Contact-Less Real-Time Monitoring of Agricultural Risk Using AI and Fuzzy Inference. Information, 10(1), 9.
15. Maraveas, C., et al. (2023). Application of Bio-Inspired Algorithms in Agricultural Engineering. Discover Agriculture, 2(1), 15–28.
16. Assimakopoulos, F., et al. (2025). AI and Related Technologies in Smart Agriculture: A Review. Information, 16(2), 45–62.
17. Raj, M., & Prahadeeswaran, M. (2025). Revolutionizing Agriculture: A Review of Smart Farming Technologies. Discover Applied Sciences, 3(4), 112–124.
18. Chokhat, A. D., et al. (2024). Agriculture Yield Prediction: AI-Driven Optimization for Sustainable Farming. International Journal of Agricultural Research, 19(2), 77–85.
19. Frontiers in AI (2024). Next-Gen Agriculture: Integrating AI and XAI for Precision Crop Yield Forecasting. Frontiers in Artificial Intelligence, 7, 112–124.
20. Dasgupta, S., et al. (2023). Genetic Algorithm-Based Feature Selection for Crop Yield Prediction. Applied Intelligence, 53(5), 345–356.